Google's Art & Culture app has been around since 2016, but the latest update harnesses machine-learning technology for an interesting purpose: it can now help you find your doppelgänger in the art world using a selfie photo.
Want to change winter to summer or day to night in a photo? In the future, that could be as easy as a single click. Scientists at NVIDIA are working on a mind-boggling AI system that can change things like seasons, time of day, and weather in photos.
Adobe just revealed a powerful new tool that's coming soon to Photoshop CC. Called "Select Subject," it uses machine learning to detect subjects in your photos to help you isolate them from the backgrounds.
Want a faster way to add relevant hashtags to your Instagram photos? Photerloo wants to help. The photo management service has launched a new free web tool that uses machine learning to automatically keywords for any photo.
As the worlds of artificial intelligence and digital photography collide, we're starting to see some mind-blowing technology emerge. The latest research in turning low-resolution photos into high-definition photos may drop your jaws -- it's starting to cross into the realm of sci-fi.
Want to turn your smartphone snapshots into DSLR-quality photos? A group of scientists in Switzerland is trying to help make that possible. They've created a neural network that aims to automatically enhance low-quality phone snapshots into "DSLR-quality photos."
Adobe is teasing an impressive new technology called Scribbler. It's an "interactive deep learning-based image generation system" that you can use to automatically colorize black-and-white photos. Above is a 6.5-minute demo of Scribbler.
The stock photo service Shutterstock has announced a new powerful photo search tool called Composition Aware Search. It uses advanced deep learning technology to let you search for photos containing certain objects in certain locations.
What if your camera could professionally retouch your photos... before you even shoot them? That's what researchers at Google and MIT are currently working on.
Can machines be trained to create photos that most people would think were shot by a professional human photographer? That's what Google is trying to figure out with a machine learning research project.
Google has been steadily adding features to their Google Photos app since I/O 2017. The new archive feature isn’t the most exciting of them, but it has an interesting trick up its sleeve.
Apple's big announcements of the day are mostly hardware focused—new iMacs, and that crazy powerful iMac Pro. But the Cupertino-based company also previewed the next version of its mobile operating system, iOS 11, complete with some creative and useful updates for mobile photographers.
This is amazing. The researchers at UC Berkeley who came up with the automatic colorizer algorithm we first shared back in March, 2016 have released a major update. The software now lets you team up with the algorithm to colorize complex black and white photos accurately in seconds.
The Trophy Camera is an experimental camera powered by artificial intelligence that can only shoot images that it deems to be "award-winning."
Videos are made up of about 24 frames every second, but what would happens if you fed a single photo into a motion prediction algorithm, and asked it to create the next 100,000 frames de novo after that? This video is what happens.
The stock photography search engine Everypixel has launched a new web app called Everypixel Aesthetics that uses a neural network to tell you how awesome any photo is.
The future is exciting for mobile photography. Adobe just released this sneak peek that shows off some amazing features powered by Adobe Sensei, which uses artificial intelligence and machine learning to do edits on photos.
Despite its uncertain future, Flickr continues to add features, doing their best to remain a relevant part of today's photo sharing landscape. Their latest update adds something called "similarity search", a feature that helps you find visually similar photos without guessing at proper search terms and tags.
Google scientists are brewing technology that could one day help you to "enhance!" photos with sharper results than current software on the market. Called RAISR, the system uses machine learning for intelligent upscaling.
Earlier this week, we showed you some really interesting image editing research from the University of Edinburgh, calling it a peek at the future of Photoshop. Well, it turns out we were more right than we realized.
Well, you can add "captioning photos" to the list of jobs robots will soon be able to do just as well as humans. After some training, the latest version of Google's "Show and Tell" algorithm can describe the contents of a photo with staggering 94% accuracy.
Researchers from Harvard and the University of Vermont have found a way to use Instagram to detect depression. Using machine learning tools, they developed a model that can predict whether a person is clinically depressed with surprising accuracy, just by looking at their Instagram photos.
Want a taste of the future? There's a new web app that uses advanced "deep learning" research to magically auto-colorize black-and-white photos.
"To me, photography is the simultaneous recognition, in a fraction of a second, of the significance of an event." — Henri Cartier Bresson
With over a trillion photos created every year now, one feature that could help people make sense of their massive photo collections could be object recognition and automatic tagging. If your camera and photo management software can figure out what's in your shots, it'll make searching through old photos much more easy and intuitive.
Companies and researchers are working hard on pushing this field forward. Photo sharing services are already adding auto-tagging to their systems -- Flickr and Google had to work out some early "racist" bugs -- and now we're getting a glimpse of what the technology could look like live, in cameras.
Before you head out for your next vacation, you may want to consider what your photos on Flickr reveal about your travel plans. A new study published in the Royal Society Open Science journal used machine learning algorithms to model the mobility of individuals.
By analyzing the embedded timestamp and geographic information within photographs, the researchers were able to accurately predict where a person is most likely currently located and where they may be headed in the future.
A group of Google researchers have created a new system called DeepStereo that can predict views of scenery it has never seen before. The technology allows a collection of images to be turned into a three-dimensional walkthrough using sophisticated algorithms to fill in areas that were absent from the photographs -- interpolating missing frames for a smooth experience.
The 2-minute video above shows some examples of what the system can create using Street View-style still photos.
Flickr sparked some controversy back in May after it was discovered that the service's new autotagging feature was prone to mislabeling black people as "apes." It looks like Google Photos developers didn't learn from Flickr's embarrassing misstep: a Google developer is apologizing after it was found that Google's Photos app misidentifies photos of black people as "gorillas."
One of the emerging trends in the world of photography is the idea of automated recognition and tagging of things found in photographs. Flickr can now suggest autotags for your photos, and Google's new Photos service lets you search through your unlabeled photos using advanced image recognition.
The same technologies are coming for real-time camera features as well. Qualcomm is working on a system called SceneDetect that lets cameras recognize what they're looking at in real time.
Camera apps these days already have the ability to analyze your scenes before you shoot them, but what if they could analyze your food before you eat it? That's what Google researchers are working on: they're trying to teach a computer to calculate calories from ordinary snapshots of food.
Scientists at Google Research and Stanford University have teamed up to develop an artificial intelligence program designed to automatically produce captions based on the content of the image.
That's right, not just tags, full on captions like "A person riding a motorcycle on a dirt road."